Loss Capture Ratio

src.meliora.core.loss_capture_ratio(ead, predicted_ratings, realised_outcomes)

The loss_capture_ratio measures how well a model is able to rank LGDs when compared to the observed losses. For this approach three plots are relevant: the model loss capture curve, ideal loss capture curve and the random loss capture curve. These curves are constructed in the same way as the curves for the CAP. The main difference is the data, which is for LGDs and the LR a (continuous) percentage of the EAD, while for the CAP it is binary. The LC can be percentage weighted, which simply uses the LGD and LR percentages as input, while it can also be EAD weighted, which uses the LGD and LR multiplied with the respective EAD as input. The results between the two approaches can differ if the portfolio is not-well balanced. :param ead: Exposure at Default :type ead: pandas Series :param predicted_ratings: predicted LGD, can be ordinal or continuous :type predicted_ratings: pandas Series :param realised_outcomes: realised LGD, can be ordinal or continuous :type realised_outcomes: pandas Series

Returns

LCR – Loss Capture Ratio

Return type

scalar

References

Li, D., Bhariok, R., Keenan, S., & Santilli, S. (2009). Validation techniques and performance metrics for loss given default models. The Journal of Risk Model Validation, 33, 3-26.

Examples

>>> res = loss_capture_ratio(ead, predicted_ratings, realised_outcomes)
>>> print(res)